Electroencephalographic neuromonitoring is a valuable non-invasive tool and the most cost-effective means for diagnosis and treatment of various neurological disorders. However, EEG signals are very susceptible to various artifacts and environmental noise, which can seriously impede their visual or automated analysis and interpretation. Methods currently employed for removing artifacts from EEG recordings are not clinically effective or feasible for real-time and long-term neuromonitoring. The overall goal of this project is to develop a real-time, fully-automated signal processing technique for high-fidelity identification and removal of artifacts that commonly contaminate EEG recordings. Our aim is to offer a comprehensive, commercially viable and user-friendly research/clinical software package for a variety of clinical monitoring applications and systems, including epilepsy, sleep disorders, neurological injuries, depth of anesthesia, and psychiatric disorders. The development and implementation of these novel methods will also significantly enhance the functionality of ambulatory EEG/PSG monitoring systems such as Cleveland Medical Devices' wireless monitors. The developed algorithms will be integrated in our entire line of EEG\PSG devices. This will allow realization of the full potential of these wirelessVambulatory monitors. It will further open new possibilities for automated neuromonitoring in real time, and for eventual intervention systems. [unreadable] [unreadable]